Search Results for author:
Found 9 papers, 5 papers with code
Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation
On the real-world task of battery material phase segmentation, PaSeR yields a minimum performance improvement of 174% on the IoU/GigaFlop metric with respect to baselines.
Attention for Causal Relationship Discovery from Biological Neural Dynamics
This paper explores the potential of the transformer models for learning Granger causality in networks with complex nonlinear dynamics at every node, as in neurobiological and biophysical networks.
Snapshot Multispectral Imaging Using a Diffractive Optical Network
Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially-repeating virtual spectral filter array with 2x2=4 unique bands at terahertz spectrum.
Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks
Permutation matrices form an important computational building block frequently used in various fields including e. g., communications, information security and data processing.
Super-resolution image display using diffractive decoders
We report a deep learning-enabled diffractive display design that is based on a jointly-trained pair of an electronic encoder and a diffractive optical decoder to synthesize/project super-resolved images using low-resolution wavefront modulators.
ECG Heartbeat Classification Using Multimodal Fusion
We achieved classification accuracy of 99. 7% and 99. 2% on arrhythmia and MI classification, respectively.
ECG Heart-beat Classification Using Multimodal Image Fusion
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal.
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks
We show that by providing the medical practitioners with a tool to tune two hyperparameters of the Bayesian neural network, namely, fraction of sampled number of networks and minimum probability, the framework can be adapted as needed by the domain expert.
